HyHOPE: Hybrid Head Orientation and Position Estimation for vision-based driver head tracking
Erik Murphy-Chutorian, Mohan Manubhai Trivedi · 2008
Driver distraction and inattention are prominent causes of automotive collisions. To enable driver assistance systems to address these problems, we require new sensing approaches to infer a driverpsilas focus of attention. In this paper, we present a new 3D tracking algorithm and integrate it into HyHOPE, a real-time (30 fps) hybrid head orientation and position estimation system for driver head tracking. With a single video camera, the system continuously tracks the head in six degrees-of-freedom, initializing itself automatically with separate modules for head detection and head pose estimation. The tracking module provides a fine estimate of the 3D motion of the head, using a new appearance-based algorithm for 3D model tracking by particle filtering in an augmented reality environment. We describe our implementation, which utilizes OpenGL-optimized graphics hardware to efficiently compute particle samples in real-time. To quantitatively evaluate the accuracy of our system, we compare its estimation results to a marker-based cinematic motion capture system installed in an automotive testbed. We evaluate the system on real daytime and nighttime drives with drivers of varying ages, race, and sex.